Study on the Strengths and Weaknesses of Agricultural Climate Resources during Summer Drought in Guizhou Province

In order to quantitatively assess the objective impact of light,heat and water agricultural climate resources on food crops during summer drought,this paper uses the assessment methods for light and temperature potential productivity,and light,temperature and water potential productivity of food crops,performs the comparative analysis of the difference between the food production potential and the average climate state during summer drought,and objectively analyzes the strengths and weaknesses of agricultural climate resources in Guizhou Province during summer drought. Studies show that under summer drought in Guizhou Province,the light and temperature potential productivity of rice and corn is generally about 10% higher than in normal climate years,and the strengths of light and heat resources are obvious; the light,temperature and water potential productivity of rice and corn is generally 30% to 40% lower than in normal climate years,and the weaknesses of water resources hamper the crop growth. Rational development and efficient use of water resources and good light and heat conditions for crops during drought,are more conducive to agricultural production.

A Comparison of Four Neural Networks Algorithms on Locomotion Intention Recognition of Lower Limb Exoskeleton Based on Multi-source Information

Lower Limb Exoskeletons(LLEs)are receiving increasing attention for supporting activities of daily living.In such active systems,an intelligent controller may be indispensable.In this paper,we proposed a locomotion intention recognition system based on time series data sets derived from human motion signals.Composed of input data and Deep Learning(DL)algorithms,this framework enables the detection and prediction of users’movement patterns.This makes it possible to predict the detection of locomotion modes,allowing the LLEs to provide smooth and seamless assistance.The pre-processed eight subjects were used as input to classify four scenes:Standing/Walking on Level Ground(S/WOLG),Up the Stairs(US),Down the Stairs(DS),and Walking on Grass(WOG).The result showed that the ResNet performed optimally compared to four algorithms(CNN,CNN-LSTM,ResNet,and ResNet-Att)with an approximate evaluation indicator of 100%.It is expected that the proposed locomotion intention system will significantly improve the safety and the effectiveness of LLE due to its high accuracy and predictive performance.